mlsanityData to 7 Oct 2026

Explainable artificial intelligence model to predict acute critical illness from electronic health records

arXiv 1912.01266 ↗First posted 3 Dec 2019cs.AI cs.LG stat.APcited by 42

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Abstract (arXiv)

We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on EHR-trained AI systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. In this letter, we present our xAI-EWS system, which potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2019.

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Citing papers per year: 2021: 7; 2022: 11; 2023: 11; 2024: 3; 2025: 6; 2026: 4.

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  1. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  2. 2023CLIP in Medical Imaging: A Surveycited by 40
  3. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  4. 2022WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Valuescited by 24
  5. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  6. 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
  7. 2023The role of causality in explainable artificial intelligencecited by 17
  8. 2023Remote patient monitoring using artificial intelligence: Current state, applications, and challengescited by 14
  9. 2022Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteriacited by 11
  10. 2021Offline reinforcement learning with uncertainty for treatment strategies in sepsiscited by 9
  11. 2025MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathwayscited by 9
  12. 2021Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Reviewcited by 5
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  1. 2022Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Recordscited by 5
  2. 2021Coalitional strategies for efficient individual prediction explanationcited by 4
  3. 2022An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning modelscited by 4
  4. 2021Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoffcited by 3
  5. 2023MLOps Spanning Whole Machine Learning Life Cycle: A Surveycited by 3
  6. 2022Modelling Patient Trajectories Using Multimodal Informationcited by 2
  7. 2022Using explainability to design physics-aware CNNs for solving subsurface inverse problemscited by 2
  8. 2023A Brief Review of Explainable Artificial Intelligence in Healthcarecited by 2
  9. 2023Interpreting Deep Neural Networks with the Package innsightcited by 2
  10. 2023Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalitiescited by 2
  11. 2025DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentationcited by 2
  12. 2021The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wardscited by 1
  13. 2021Simple black-box universal adversarial attacks on medical image classification based on deep neural networkscited by 1
  14. 2022Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision makingcited by 1
  15. 2023NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patientscited by 1
  16. 2023Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?cited by 1
  17. 2024Applications of Explainable artificial intelligence in Earth system sciencecited by 1
  18. 2025Explainable AI For Early Detection Of Sepsiscited by 1
  19. 2022Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillationcited by 0
  20. 2022Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcarecited by 0
  21. 2023Echoes of Biases: How Stigmatizing Language Affects AI Performancecited by 0
  22. 2024Reading ability detection using eye-tracking data with LSTM-based few-shot learningcited by 0
  23. 2024Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devicescited by 0
  24. 2025Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Carecited by 0
  25. 2025Towards a Transparent and Interpretable AI Model for Medical Image Classificationscited by 0
  26. 2025Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypescited by 0
  27. 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attackscited by 0
  28. 2026YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perceptioncited by 0
  29. 2026Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injurycited by 0
  30. 2026ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoningcited by 0